Leveraging Digital Twin for Real-Time Monitoring and Optimization in Civil Engineering
Assessing the Performance of Consultants in Public Projects in Southern Region of Ethiopia: A Case Study in Wolaita Zone
Rainfall Patterns and Its Impact on Storm Water Runoff using Weibull Distribution Method: A Case Study of Dahisar, Mumbai, India
A Comparison Study between Normal Concrete and Self-Compacting Concrete with Copper Slag and Steel Fibres
Risk Reduction in Construction Projects through Datadriven Technology: A Literature Review
Revisiting Solid Waste Management in India: Current Trends and Future Prospects
Estimating the Soil Moisture Index using Normalized Difference Vegetation Index (NDVI) And Land Surface Temperature (LST) for Bidar and Kalaburagi District, Karnataka
Site Suitability Analysis for Solid Waste Dumping in Ranchi City, Jharkhand Using Remote Sensing and GIS Techniques
Roughness Evaluation of Flexible Pavements Using Merlin and Total Station Equipment
Unsaturated Seepage Modeling of Lined Canal Using SEEP/W
Strengthening and Rehabilitation of RC Beams with Openings Using CFRP
A Seasonal Autoregressive Model Of Vancouver Bicycle Traffic Using Weather Variables
Prediction of Compressive Strength of Concrete by Data-Driven Models
Predicting the 28 Days Compressive Strength of Concrete Using Artificial Neural Network
Measuring Compressive Strength of Puzzolan Concrete by Ultrasonic Pulse Velocity Method
Design and Analysis of Roller Compacted Concrete Pavements for Low Volume Roads in India
In recent years, research on digital twins in the construction sector has grown significantly, particularly since 2018. However, most studies focus on isolated lifecycle phases and give limited attention to developing practices that address all stages of the building lifecycle. Current applications of digital twins in construction are largely concentrated on design and engineering, where Building Information Modeling (BIM) serves as the primary input. This dominance stems from BIM's role as an existing digital representation of built assets, which can be extended into a digital twin. One of the major barriers to wider implementation lies in data integration and interoperability—specifically, the challenge of consolidating information from diverse sources, formats, and lifecycle stages. Future research should prioritize the standardization of data formats, protocols, and application programming interfaces (APIs) to facilitate seamless exchange across digital twin systems. Additionally, the development of advanced data governance frameworks is essential to ensure accuracy, completeness, and security of information. Greater exploration into semantic data modeling and ontologies will also support the integration of heterogeneous data sources. Beyond technical issues, it is equally important to investigate the costs, benefits, and potential drawbacks of adopting digital twins across the entire building lifecycle. Expanding the scope of applications beyond design and engineering to include construction, operation and maintenance, as well as demolition and recovery phases, will be crucial. This paper therefore outlines the current state of digital twin research in construction, identifies critical challenges, and highlights key directions for future development.
In the construction industry, consultants play a vital role in ensuring smooth project implementation and progress. However, consultancy performance often falls short of expectations, leading to delays. This study assesses the performance of construction consultants in the Wolaita Zone. Objectives include evaluating their roles in public projects, identifying factors affecting performance, and exploring best practices for improvement. A descriptive survey design was used, employing questionnaires and interviews, with respondents selected through simple random sampling. Data were analyzed using descriptive statistics and ranked by relative importance index. Many public projects in the Wolaita Zone perform poorly due to project-specific causes. Weaknesses include progress reporting, financial forecasting, problem-solving, design alternatives, cost estimation, tender documentation, contract administration, and site staff management. Factors such as decision-making ability, teamwork, project type and duration, planning, scheduling, and team relations also influence outcomes. Further challenges include fluctuating client needs, change orders, weak monitoring, lack of feedback, poor contract administration, limited training, and consultant incompetence. The study found that management, project, cost, and client satisfaction factors most significantly affect consultant performance. Based on the findings, appropriate solutions are recommended.
This research investigates the effects of urbanization on stormwater runoff in Dahisar, Mumbai, India, over the period 2003–2023. NASA POWER data, along with the Weibull distribution method, is employed to assess long-term trends in rainfall and temperature. The study highlights a notable rise in impervious surfaces driven by urban growth, as identified through the Weibull distribution analysis. The peak stormwater discharge is estimated at 1433.44 cubic meters per second, highlighting the growing risk of urban flooding. The findings indicate an annual average rainfall of 1720 mm, which directly influences stormwater dynamics and drainage capacity. The study underscores the pressing need for sustainable urban planning strategies that integrate resilient water management solutions. Green infrastructure, permeable pavements, and improved drainage networks are recommended to mitigate the adverse effects of rapid urbanization on stormwater systems. As Mumbai continues to expand, addressing these hydrological challenges is essential to reduce flood risks and enhance climate resilience. The study provides valuable insights for policymakers, urban planners, and environmental engineers, advocating for adaptive measures to improve urban water management in coastal megacities.
This study explores and compares the performance of two concretes: Normal Concrete (NC) and Self-Compacting Concrete (SCC). It focuses on using copper slag (CS) as a partial replacement for sand and adding steel fiber to improve strength. A total of 16 concrete mixes were created: eight SCC mixes (M1 to M8) and eight NC mixes (M10 to M40) with copper slag replacing sand in amounts from 0% to 100% and different types of steel fiber added. The results showed that self-compacting concrete with copper slag had excellent flow characteristics, achieving a slump flow of 690 mm without any segregation. At this level, the compressive strength increased by 9%, from 60.8 MPa to 65.73 MPa. However, using 100% copper slag reduced the strength to 49.72 MPa, a 20% decrease. At the 30% replacement level, the flexural strength improved by 4.5%, and the split tensile strength improved by 3%. In the normal concrete mixes, adding 0.5% of crimped steel fiber (aspect ratio 53.85) gave the best result, increasing the compressive strength by up to 18.16% compared to concrete without fiber. This fiber also helped control cracks, improving both tensile and flexural strength. Overall, the best performance in both SCC & NC was observed when 30% of sand was replaced with copper slag. Self- compacting concrete had better workability and could compact itself without vibration, while NC with steel fibers showed better resistance to cracking. These findings support the use of industrial byproducts and fibers to make concrete more sustainable, durable, and structurally efficient.
The integration of data-driven technologies within construction project management has emerged as a pivotal strategy for risk reduction. Project managers can identify potential risks early by applying advanced analytics, machine learning, and artificial intelligence so that they will take prompt action with proper decision making. This makes it easy to plan for roadblocks as well as resource distribution, thereby enhancing project outcomes. However, importantly, these technologies augment human ability rather than replace it and turning the insights of data into actionable plans requires data analysts, engineers, project managers to work together. Continuously researching and investing in more advanced data-driven tools are key to the evolution of risk management strategies in construction. By reaping the benefits of technology and staying ahead of trends, construction companies can take on complexities and uncertainties with great assurance. This makes us a continuous development and excellence organization. The study highlights the importance of preventing risks by using data-driven technologies to improve communication, decision-making and project efficiency. Suggestions for future research includes moving towards complex predictive analytics models and integrating AI and VR to enhance risk assessment and training, with the aim of developing a safer and more resilient built environment.
This study examines the current state of SWM in India, a country facing unique challenges due to its large population and high waste generation rates. The study highlights the inefficiencies in traditional waste disposal methods, such as open dumping and burning, and reviews existing regulations governing waste management in India. Emphasizing the importance of waste segregation at the source, the adoption of sustainable practices such as composting and recycling, and the implementation of advanced technologies, this research underscores the necessity for a collaborative approach involving government, industry, and communities to develop effective and sustainable waste management strategies. By addressing these challenges, India can set a precedent for other developing nations, fostering a cleaner environment and promoting public health and economic growth. The findings of this research will provide valuable insights into the feasibility and optimal utilization of SSA in concrete, paving the way for its application as a viable construction material while addressing environmental concerns associated with sewage sludge disposal.
